Critical Evaluation of a microRNA-Based Risk Classifier Predicting Cancer-Specific Survival in Renal Cell Carcinoma with Tumor Thrombus of the Inferior Vena Cava

Simple Summary Renal cell carcinomas (RCCs) can build a so-called tumor thrombus and grow into the renal vein and the vena cava, thereby representing a high-risk situation for affected patients. Nevertheless, there are substantial differences in the clinical courses of RCC patients with a tumor thrombus. Currently, there is no established biomarker which helps identifying patients with a substantial risk of tumor relapse or even cancer-related death. Previously, members of our group discovered a signature of three small RNA molecules (specifically: microRNAs) in RCC tissue, which significantly predicted cancer-related survival. In this study, we validated this signature in a larger cohort of patients suffering from RCCs with a tumor thrombus. Notably, stratifying our patients according to this microRNA signature nearly separated our cohort into two halves, which significantly differed in terms of clinical risk. Our research could help identifying high-risk patients in need for additional therapy, while sparing others from unnecessary treatments. Abstract (1) Background: Clear cell renal cell carcinoma extending into the inferior vena cava (ccRCCIVC) represents a clinical high-risk setting. However, there is substantial heterogeneity within this patient subgroup regarding survival outcomes. Previously, members of our group developed a microRNA(miR)-based risk classifier—containing miR-21-5p, miR-126-3p and miR-221-3p expression—which significantly predicted the cancer-specific survival (CSS) of ccRCCIVC patients. (2) Methods: Examining a single-center cohort of tumor tissue from n = 56 patients with ccRCCIVC, we measured the expression levels of miR-21, miR-126, and miR-221 using qRT-PCR. The prognostic impact of clinicopathological parameters and miR expression were investigated via single-variable and multivariable Cox regression. Referring to the previously established risk classifier, we performed Kaplan–Meier analyses for single miR expression levels and the combined risk classifier. Cut-off values and weights within the risk classifier were taken from the previous study. (3) Results: miR-21 and miR-126 expression were significantly associated with lymphonodal status at the time of surgery, the development of metastasis during follow-up, and cancer-related death. In Kaplan–Meier analyses, miR-21 and miR-126 significantly impacted CSS in our cohort. Moreover, applying the miR-based risk classifier significantly stratified ccRCCIVC according to CSS. (4) Conclusions: In our retrospective analysis, we successfully validated the miR-based risk classifier within an independent ccRCCIVC cohort.